Nanorobots are untethered structures of sub-micron size that can be controlled in a non-trivial way. Such nanoscale robotic agents are envisioned to revolutionize medicine by enabling minimally invasive diagnostic and therapeutic procedures. To be useful, nanorobots must be operated in complex biological fluids and tissues, which are often difficult to penetrate. In this chapter, we first discuss potential medical applications of motile nanorobots. We briefly present the challenges related to swimming at such small scales and we survey the rheological properties of some biological fluids and tissues. We then review recent experimental results in the development of nanorobots and in particular their design, fabrication, actuation, and propulsion in complex biological fluids and tissues. Recent work shows that their nanoscale dimension is a clear asset for operation in biological tissues, since many biological tissues consist of networks of macromolecules that prevent the passage of larger micron-scale structures, but contain dynamic pores through which nanorobots can move.

Haptics is an interdisciplinary field that seeks to both understand and engineer touch-based interaction. Although a wide range of systems and applications are being investigated, haptics researchers often concentrate on perception and manipulation through the human hand.
A haptic interface is a mechatronic system that modulates the physical interaction between a human and his or her tangible surroundings. Haptic interfaces typically involve mechanical, electrical, and computational layers that work together to sense user motions or forces, quickly process these inputs with other information, and physically respond by actuating elements of the user’s surroundings, thereby enabling him or her to act on and feel a remote and/or virtual environment.

An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action).
A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g. educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system.

Since the 1950s, robotics research has sought to build a general-purpose agent capable of autonomous, open-ended interaction with realistic, unconstrained environments. Cognition is perceived to be at the core of this process, yet understanding has been challenged because cognition is referred to differently within and across research areas, and is not clearly defined. The classic robotics approach is decomposition into functional modules which perform planning, reasoning, and problem-solving or provide input to these mechanisms. Although advancements have been made and numerous success stories reported in specific niches, this systems-engineering approach has not succeeded in building such a cognitive agent.
The emergence of an action-oriented paradigm offers a new approach: action and perception are no longer separable into functional modules but must be considered in a complete loop. This chapter reviews work on different mechanisms for action- perception learning and discusses the role of embodiment in the design of the underlying representations and learning. It discusses the evaluation of agents and suggests the development of a new embodied Turing Test. Appropriate scenarios need to be devised in addition to current competitions, so that abilities can be tested over long time periods.

Designing a Brain Computer Interface (BCI) system one can choose from a variety of features that
may be useful for classifying brain activity during a mental task. For the special case of classifying EEG signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination [3] and Zero-Norm Optimization [13] which are based on the training of Support Vector Machines (SVM) [11]. These algorithms can provide more accurate solutions than standard filter methods for feature selection [14].
We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we
show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks.
Furthermore we show how time dependent task specific information can be visualized.

The Google search engine has had a huge success with its PageRank
web page ranking algorithm, which exploits global, rather than
local, hyperlink structure of the World Wide Web using random
walk. This algorithm can only be used for graph data, however.
Here we propose a simple universal ranking algorithm for vectorial
data, based on the exploration of the intrinsic global geometric
structure revealed by a huge amount of data. Experimental results
from image and text to bioinformatics illustrates the validity of
our algorithm.

A new method for performing a kernel principal component analysis is
proposed. By kernelizing the generalized Hebbian algorithm, one can
iteratively estimate the principal components in a reproducing
kernel Hilbert space with only linear order memory complexity. The
derivation of the method, a convergence proof, and preliminary
applications in image hyperresolution are presented. In addition,
we discuss the extension of the method to the online learning of
kernel principal components.

We consider the learning problem in the transductive setting. Given
a set of points of which only some are labeled, the goal is to
predict the label of the unlabeled points. A principled clue to
solve such a learning problem is the consistency assumption that a
classifying function should be sufficiently smooth with respect to
the structure revealed by these known labeled and unlabeled points. We present a simple
algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a
number of classification problems and demonstrates effective use of
unlabeled data.

The Wiener series is one of the standard methods to systematically
characterize the nonlinearity of a neural system. The classical
estimation method of the expansion coefficients via cross-correlation
suffers from severe problems that prevent its application to
high-dimensional and strongly nonlinear systems. We propose a new
estimation method based on regression in a reproducing kernel Hilbert
space that overcomes these problems. Numerical experiments show
performance advantages in terms of convergence, interpretability and
system size that can be handled.

A key tool in protein function discovery is the ability to rank databases of proteins given a query amino acid sequence. The most successful method so far is a web-based tool called PSI-BLAST which uses heuristic alignment of a profile built using the large unlabeled database. It has been shown that such use of global information via an unlabeled data improves over a local measure derived from a basic pairwise alignment such as performed by PSI-BLAST's predecessor, BLAST. In this article we
look at ways of leveraging techniques from the field of machine learning for the problem of ranking. We show how clustering and semi-supervised learning techniques, which aim to capture global structure in data, can significantly improve over PSI-BLAST.

Canonical correlation analysis (CCA) is a classical multivariate method concerned with describing linear dependencies between sets of variables. After a short exposition of the linear sample CCA problem and its analytical solution, the article proceeds with a detailed characterization of its geometry. Projection operators are used to illustrate the relations between canonical vectors and variates. The article then addresses the problem of CCA between spaces spanned by objects mapped into kernel feature spaces. An exact solution for this kernel canonical correlation (KCCA) problem is derived from a geometric point of view. It shows that the expansion coefficients of the canonical vectors in their respective feature space can be found by linear CCA in the basis induced by kernel principal component analysis. The effect of mappings into higher dimensional feature spaces is considered critically since it simplifies the CCA problem in general. Then two regularized variants of KCCA are discussed. Relations to other methods are illustrated, e.g., multicategory kernel Fisher discriminant analysis, kernel principal component regression and possible applications thereof in blind source separation.

We introduce two new functions, the kernel covariance (KC) and the kernel
mutual information (KMI), to measure the degree of independence of several
continuous random variables.
The former is guaranteed to be zero if and only if the random variables
are pairwise independent; the latter shares this property, and is in addition
an approximate upper bound on the mutual information, as measured near
independence, and is based on a kernel density estimate.
We show that Bach and Jordan‘s kernel generalised variance (KGV) is also
an upper bound on the same kernel density estimate, but is looser.
Finally, we suggest that the addition of a regularising term in the KGV
causes it to approach the KMI, which motivates the introduction of this
regularisation.
The performance of the KC and KMI is verified in the context of instantaneous
independent component analysis (ICA), by recovering both artificial and
real (musical) signals following linear mixing.

In this short note, building on ideas of M. Herbster [2] we propose a method for automatically tuning the
parameter of the FIXED-SHARE algorithm proposed by Herbster and
Warmuth [3] in the context of on-line learning with
shifting experts. We show that this can be done with a memory
requirement of $O(nT)$ and that the additional loss incurred by
the tuning is the same as the loss incurred for estimating the
parameter of a Bernoulli random variable.

Interactive Images are a natural extension of three recent developments: digital photography, interactive web pages, and browsable video. An interactive image is a multi-dimensional image, displayed two dimensions at a time (like a standard digital image), but with which a user can interact to browse through the other dimensions. One might consider a standard video sequence viewed with a video player as a simple interactive image with time as the third dimension. Interactive images are a generalization of this idea, in which the third (and greater) dimensions may be focus, exposure, white balance, saturation, and other parameters. Interaction is handled via a variety of modes including those we call ordinal, pixel-indexed, cumulative, and comprehensive. Through exploration of three novel forms of interactive images based on color, exposure, and focus, we will demonstrate the compelling nature of interactive images.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems